Probabilistic Risk Assessment of the Environmental Impacts of Pesticides in the Crocodile (West) Marico Catchment, North-West Province

Home , Cyromazine

Probabilistic risk assessment of the environmental impacts of pesticides in the Crocodile (west) Marico catchment, North-West Province

TM Ansara-Ross1*, V Wepener1, PJ Van den Brink2, 3 and MJ Ross1 1 Centre for Aquatic Research, Department of Zoology, University of Johannesburg, Auckland Park, PO Box 524, Johannesburg 2006, South Africa 2Wageningen University, Department of Aquatic Ecology and Water Quality Management, Wageningen University and Research centre, PO Box 47, 6700 AA Wageningen, The Netherlands 3Alterra, Wageningen University and Research centre, PO Box 47, 6700 AA Wageningen, The Netherlands

Abstract

External agricultural inputs, such as pesticides, may pose risks to aquatic ecosystems and affect aquatic populations, communities and ecosystems. To predict these risks, a tiered approach was followed, incorporating both the PRIMET and PERPEST models. The first-tier PRIMET model is designed to yield a relatively worst-case risk assessment requiring a minimum of input data, after which the effects of the risks can be refined using a higher tier PERPEST model. The risk assessment initially depends on data supplied from local landowners, pesticide characteristic, application scheme and physical scenario of the environment under question. Preliminary results are presented, together with ecotoxicological data on several frequently-used pesticides in a section of the Crocodile (west) Marico Water Management Area (WMA) in South Africa. This area is historically known to have a high pesticide usage, with deltamethrin, aldicarb, parathion, cypermethrin and dichlorvos being the main pesticides used. Deltamethrin was indicated as having the highest probability of risks to aquatic organisms occurring in the study area. Cypermethrin, parathion, dichlorvos, carbaryl, bromoxynil, linuron, methomyl and aldicarb were all indicated as having possible risks (ETR 1-100) to the aquatic environment. Pesticides posing no risk included fenamiphos, abamectin, pendimethalin, captan, endosulfan, alachlor, bentazone and cyromazine (ETR<1). The pesticides posing a possible risk to the aquatic ecosystem were evaluated further to determine their effects on 8 grouped endpoints using the PERPEST effect model. Deltamethrin and cypermethrin were again noted as posing the greatest risk and clear effects were eminent for aquatic insects and macro-crustaceans, followed by micro-crustaceans and rotifers. High percentages of clear effects on insects were also observed for carbaryl, parathion and dichlorvos. Linuron was indicated as having minimal clear effects on community metabolism, macrophytes and phytoplankton classes, while lesser clear effects of bromoxynil occurred on periphyton communities. Application of both the lower-tier PRIMET and higher-tier PERPEST models showed similar trends in that they both ranked the top 5 pesticides in the same order of risk. This approach offers a significant improvement over the presently-used simulation models or use of safety factors. It is therefore especially useful in developing countries such as South Africa, where pesticide environmental risk information is scarce. Although these models were effectively used in this study, it still has to be validated further under South African conditions

Keywords: risk-assessment model, pesticides, aquatic ecosystem

Introduction (e.g. Schulz (2001) and Bollmohr et al. (2007)) focus primarily on pesticide exposures and effects in estuaries and rivers within Agriculture forms an important component of South Africa’s the Western Cape. These authors, however, do not report on the economy, with extrinsic inputs, like pesticides and fertilisers, application of a probabilistic modelling approach for pesticide helping to ensure a reliable and high agricultural yield. The use of risk assessment for South Africa. such agrochemicals has benefited the quality of human health in Present techniques used in assessing the ecological fate terms of food security and quality. Extensive and improper appli- and effect of pesticides under realistic field conditions involve cation of these pesticides, however, may pose risks to aquatic eco- a large number of parameters to be measured. These can there- systems and consequently affect non-target populations, commu- fore incorporate intricate and detailed simulation models such nities and ecosystems. A large number of different pesticides are as ecological, population or food-web models (e.g. Koelmans et currently available to agriculture, most of which are applied fre- al., 2001; Traas et al., 1998; Van den Brink et al., 2007). These quently. For this reason, it is important to be able to assess which risk-assessment models often have drawbacks, which include of these chemicals pose the greatest risks to the environment. a lack of transparency, a high degree of complexity that can As far as could be ascertained, no risk-assessment studies cause compounding errors through erroneous primary data, and using probabilistic models have been undertaken within South expensive implementation thereof (Van den Brink et al., 2006; Africa to determine the effects of pesticides to the freshwater Van der Werf, 1996). The intricate input data required are also aquatic environment. The studies that have been undertaken unavailable for many pesticides. These models also very often focus only on certain aspects of risk assessment and there- * To whom all correspondence should be addressed. fore have limited application. These aspects pose restrictions,  +2711 467 2931; fax: +2711 467 2931; especially for developing countries that lack the resources (Lon- e-mail: [email protected] don et al., 2005).

Available on website http://www.wrc.org.za 637 ISSN 0378-4738 = Water SA Vol. 34 No. 5 October 2008 ISSN 1816-7950 = Water SA (on-line) The development of the PRIMET and PERPEST models was field experiments and includes actual case scenarios, whilst the aimed at surpassing these limitations by being centred on deliver- SSD concept is based on laboratory data only. ing results of increased scope of application, with limited input The PERPEST model (Van den Brink et al., 2002; Van Nes data being required to evaluate the risks of pesticides to the envi- and Van den Brink, 2003; Van den Brink et al., 2006) predicts the ronment. This allows for people without specialist training in the toxic effects of a particular concentration of a pesticide on grouped scientific field to utilise the models for monitoring as well as for endpoints. The PERPEST expert model is based on a case-based management applications (e.g. farmers). reasoning (CBR) approach. This is a technique that solves new The risks of pesticides to the environment are evaluated by problems by using past experience. For developing the model, taking into account factors associated with both exposure (by cal- empirical data were extracted from published literature describ- culating the predicted concentrations) and effect assessments (by ing the results from mesocosm and microcosm experiments for determining safe concentrations based on laboratory toxicity data freshwater model ecosystem studies with pesticides (Brock et al., and the use of assessment factors) of the compound in question 2000a; 2000b) and were collated within a database. When evalu- and incorporating the quantity used and doses applied to a defined ating the effects of insecticides, the results of these experiments scenario. Application rate is known to be an important explana- are assigned to 1 of 8 endpoint categories: algae and macrophytes; tory variable determining the concentration of the pesticide in the community metabolism; fish; insects; macro-crustaceans; micro- environment (Van der Werf, 1996; Tesfamichael and Kaluarach- crustaceans; other invertebrates and rotifers. For herbicides, chi, 2006) and needs to be taken into consideration when using the 8 effect groupings are as follows: macrophytes; periphyton; risk-assessment models. This aspect is taken into consideration phytoplankton; zooplankton; community metabolism; fish and for these models. tadpoles; molluscs and macro-crustaceans and insects (Van den The PRIMET Decision Support System (Pesticide Risks in Brink et al., 2006). Except for community metabolism, all other the Tropics to Man, Environment and Trade, Van den Brink et al., effect categories represent structural endpoints, while the former 2005) can be applied to estimate the lower-tier risks of pesticide represents a functional response. When the effect of a particu- application on aquatic and terrestrial ecosystems, groundwater as lar concentration of a particular pesticide has to be predicted, the well as human health aspects - via dietary exposure by consuming PERPEST model searches for analogous situations in the data- vegetables, fish or macrophytes. In this preliminary risk assess- base based on relevant (toxicity) characteristics of the compound, ment only entry via spray drift was taken into consideration. exposure concentration and type of ecosystem to be evaluated This model uses defined scenarios and is applicable to a warmer (Van den Brink et al., 2006). This allows the model to use infor- environment, specifically in developing countries (such as South mation on other pesticides when predicting effects of a particular Africa) as it includes a temperature-dependent assessment of pesticide. The parameters for the prediction can be optimised by the exposure concentration (this is included in the degradation using a controlled random search procedure (Van den Brink et and volatilisation rate parameters such as half-life, saturated al., 2002). The PERPEST model results in a prediction showing vapour pressure and solubility). The PRIMET version 1.0 model the probability of no, slight or clear classes of effects on the vari- has already been used in developing south-east Asian countries ous grouped endpoints at a specific concentration of a pesticide. (including Thailand and Sri Lanka) and is designed to yield a rela- All grouped endpoints are simultaneously selected from the PER- tively worst-case risk assessment, requiring a minimum of input PEST model for the insecticides and herbicides indicating pos- data (Van den Brink et al., 2003; Satapornvanit et al., 2004). The sible or definite risks in the PRIMET model. This provides a more model, however, needs to be appropriately adapted to enable it to realistic estimation of effects. The PERPEST model therefore be applied and validated under South African conditions. overcomes the contentious issue surrounding the use of single- The input variables needed to feed the aquatic component of species toxicity tests for predictions in risk assessment at higher the PRIMET model were pesticide properties, site-specific condi- levels (Levitan et al., 1995) as it uses information gathered at the tions and characteristics of the pesticide application as indicated ecosystem level. by the standard agronomic practices used by farmers in the area. The PRIMET model therefore calculates whether a risk of a The output of this model is expressed in an Exposure Toxicity certain pesticide is eminent, whereas the PERPEST model quali- Ratio (ETR). This is defined as the risk ratio between the envi- fies and defines the risk. The PERPEST model refines the- out ronmental concentration (PEC) and the estimated safe concentra- come of the risk as determined from the PRIMET model. For a tion (PNEC). The value of an ETR is seen as an estimation of more detailed description on the equations and calculations used the potential risks posed by a given agrochemical for a specific for the PRIMET and PERPEST models, refer to Van den Brink et scenario (Linders and Luttik, 1995). This is represented in the al. (2005) and Van Nes and Van den Brink (2003), respectively. three classes of ‘no risk’, ‘possible risk’ or ‘definite risk’. If an Preliminary results are presented, together with ecotoxico- ETR score is less than one, the risk is acceptable and no further logical data on several frequently used pesticides in a section risk assessment would be required. If larger than 1 but less than of the upper reach of the Crocodile (west) Marico Water Man- 100, a risk may be present. If the ETR is larger than 100, a definite agement Area (WMA) in South Africa. This area is historically risk (based on the worst-case scenario) is assumed. If a possible or known to have a high pesticide usage, with aldicarb, cypermeth- definite risk is indicated in this lower tier assessment, higher tier rin, deltamethrin, dichlorvos, endosulfan and parathion being the models such as the fate models TOXSWA (TOXic substances in main pesticides used within the area as indicated by the pesti- Surface Waters, Beltman and Adriaanse, 1999) and PEARL (Pes- cide consultants within the area (Van der Merwe, year please) ticide Emission Assessment of Regional and Local Scales, Tiktak The area is intensively irrigated with the source of water being et al., 2000) and the effect models PERPEST (Predicting the Eco- from a network of canals entering and leaving the Crocodile and logical Risks of PESTicides, Van den Brink et al., 2002) and SSD Magalies Rivers. In this study, it is demonstrated how the models (Species Sensitivity Distributions, Posthuma et al., 2002), can be can be implemented to assess the potential risks of agricultural applied to establish a more realistic characterisation of risk. pesticides in South Africa, using only baseline data in an area The SSD concept as an effect assessment was not covered in known to have a high pesticide usage and where little information this paper. The authors feel that the PERPEST model is better is available. suited for field-relevant data as it is based on the results of semi- This paper addresses two research questions:

638 Available on website http://www.wrc.org.za ISSN 0378-4738 = Water SA Vol. 34 No. 5 October 2008 ISSN 1816-7950 = Water SA (on-line) TABLE 1 Pesticides used in the study area with their type of use and their physico-chemical properties

Active Pesticide type Molecu- Saturated Tem- Solubility Tem- DT50/ DT50- Kom ingredient lar mass vapour perature (g/m3) perature half-life- sedi- (ℓ/kg) pressure saturated solubil- water ment (Pa) vapour ity (days) (days) pressure (ºC) (ºC) Abamectin Insecticide, Acaricide 873.1 2.00E-07 22.5 7.00E-03 20 28 28 2842 Alachlor Herbicide 269.8 1.90E-03 25 242.00 22 149 22 117 Aldicarb Insecticide, Acaricide, 190.3 0.013 20 4.93E-03 20 10 23 17.4 Nematicide Betazone Herbicide 269.8 0.46E-03 20 0.05 20 2 14 20.3 Bromoxynil Herbicide 276.9 0.64E-03 20 0.13E-03 25 10 10 58 Captan Fungicide 300.6 1.10E-05 25 5.10 22.5 1 1 5 Carbaryl Insecticide, PGR 201.2 0.041E-03 23.5 120.00 20 14 14 34 Cypermethrin Insecticide 416.3 0.19E-06 20 4.00E-03 20 14 36 2137 Cyromazine Insecticide 166.2 0.45E-06 25 0.01E-03 22 28 63 438.5 Deltamethrin Insecticide 505.2 1.24E-08 25 0.20E-03 25 2 25 476 Dichlorvos Insecticide, Acaricide 221.0 2.666 25 0.02E06 25 0.1 2 87 Endosulfan Insecticide, Acaricide 406.9 2.30E-05 25 0.32 22 36 50 7.087E07 Fenamiphos Insecticide, Nematicide 303.4 0.12E-03 20 0.70E-03 20 0.4 50 58 Linuron Herbicide 249.1 2.00E-03 24 0.08E-03 25 262 60 232 Methomyl Insecticide, Acaricide 162.2 0.72E-03 25 5.80E04 25 8 8 12 Parathion Insecticide, Acaricide 291.3 0.89E-03 20 24.00 20 1.5 49 1764 Pendimethalin Herbicide 281.3 4.00E-03 25 0.28 25 28 90 2.4E04

• Can this first-tier model (PRIMET) be used efficiently to esti- for algae and macrophytes). This was done to extrapolate from the

mate environmental risks associated with pesticides for the EC50 level to a concentration at which no effects on the organisms study area? were expected. This assessment factor was also needed to account • What will the predicted effects of selected pesticides be on for interspecies variation and to protect indigenous aquatic popu- non-target groups of aquatic organisms using the higher-tier lations (EU, 1997; Van den Brink et al., 2005). The PNEC that PERPEST model at concentrations predicted by the PRIMET was used in the PRIMET model is regarded as conservative as model? the assessment factors were relatively large to counteract the lack of ecological realism. This was in accordance with Brock et al. Materials and methods (2000a; 2000b; 2006). The PRIMET model was used to calculate the first-tier PEC General approach and PNEC concentrations, and the resulting PEC value was evaluated further for the effects (PNEC) on 8 aquatic endpoints The general approach of methodologies followed for this study using the higher-tier PERPEST effect model (Van den Brink et involved the first-tier preliminary estimation of the risks posed by al., 2002) as described above. each pesticide to the aquatic ecosystem with the application of the PRIMET model. This was used as a screening tool for this lower- Parameters governing the fate of pesticides tier risk assessment. The PEC values for each pesticide showing applicable to the model possible or definite risks were further evaluated by the higher-tier PERPEST effect model to determine the effects on aquatic organ- Pesticide characteristics isms at these concentrations. The pesticide characteristics that were required as input data The exposure component of the PRIMET Model (understand- for the aquatic component of the PRIMET model are given in ing of the dispersion of a chemical in the environment) involved Table 1. Only active ingredients used in large quantities in the the calculation of a Predicted Environmental Concentration study area were selected. The input variables for most of these (PEC) in the watercourse, e.g. adjacent to agricultural activities. pesticides were readily available due to their widespread usage. This PEC is based on a worst-case scenario representative for Chemical properties were obtained from various open scientific local conditions. Acute risks were the focus of this study. The literature sources, if not already given in the PRIMET database.

PEC was represented by the peak concentration and not by a All relevant EC50 values available up to and including 2006 on time-weighted average exposure concentration following Van den each of these groups were extracted from databases such as the Brink et al. (2005). This PEC was then related to the effect com- U.S. EPA AQUIRE (2006); PAN (2006); EXTOXNET (2004) ponent (expected ’safe‘ concentration or a Predicted No-Effect toxicity databases, the Pesticide Manual (Tomlin, 2000) and peer- Concentration (PNEC)) based on toxicity data for selected stand- reviewed literature. The selected toxicity data were taken from ard test species from different trophic levels, namely algae (pri- acute static tests resulting in EC50 values for freshwater inverte- mary producers), Daphnia (invertebrates) and fish (vertebrates). brates (48 h), vertebrates (96 h) and primary producers (72 h and This PNEC also incorporated an assessment factor, calculated by 96 h). Effective concentration (EC) and lethal concentration (LC) multiplying the toxicity value of the most sensitive standard test were treated similarly, for reasons given in Solomon et al. (2001). species by an assessment factor (100 for fish and Daphnia and 10 Where more than one value was obtained for the same pesticide-

Available on website http://www.wrc.org.za 639 ISSN 0378-4738 = Water SA Vol. 34 No. 5 October 2008 ISSN 1816-7950 = Water SA (on-line) TABLE 2 TABLE 3 Acute toxicity data used in the PRIMET model for Physical scenario parameter values of the water standard test organisms for each taxonomic group, body defined to calculate st1 tier ETR values using namely vertebrates (96 h), invertebrates (48 h) and PRIMET primary producers (96 h) Parameter N Mean SD Active ingre- Vertebrates Invertebrates Primary Bottom width of water body (m) 3 6.1 1.49 dient L(E)C50 L(E)C50 producers a b Depth of water body (m) 3 1.5 0.5 (μg/ℓ) (μg/ℓ) L(E)C50 (μg/ℓ)a Length of water body (m/) 1 55a Abamectin 31.46E03 1.5 0.1E06 Mass fraction organic matter in 3 0.34 0.202 Alachlor 5.69E03 53.06E03 900.5 suspended solids (g/g) Aldicarb 954.8 1.79E03 nd Mass concentration of sus- 3 2.23E-05 2.02E-05 Betazone 436E03 229.7E03 279E03 pended solids in water (kg/ℓ) Ambient temperature in 3 21 5.44 Bromoxynil 11.77E03 11 7.14E03 scenario (°C) Captan 126.5 1.761E06 944.4 Side slope 3 0.4 0.2 Carbaryl 4.051E03 53.92 1.272E03 Flow velocity (m/d) 3 0.8 0.061 Cypermethrin 28.07 0.36 nd a Data taken from literature source Cyromazine 89.73E03 97.8E03 nd Deltamethrin 31.24 0.05 9.1E06 and van der Zande, 2003). A simple first-tier assessment was used Dichlorvos 1.93E03 3.25 52.8E06 for estimating spray drift when using conventional application Endosulfan 3.92 3.89 232.2 equipment. Fenamiphos 661.4 755 nd Linuron 7.91E03 310 7 Results and discussion Methomyl 1.27E03 372.7 nd Pesticides used: Amounts and chemical Parathion 1.07E03 2.99 500 characteristics Pendimethalin 3.60E03 280 72.44E03 a Aquatic toxicity L(E)C50: 96 h exposure time Environmentally relevant physico-chemical properties and char- b Aquatic toxicity L(E)C50: 48 h exposure time acteristics of the 17 pesticides used on various crops are presented nd: no data available in Table 1. These values were used to populate the models. Toxic- ity values for various trophic levels for each of the pesticides used species combination, the geometric mean toxicity value was used in the PRIMET model are given in Table 2. The parameter values to represent that taxonomic group. of the water body used to calculate the first-tier ETR values by PRIMET (for the physical scenario parameters) are presented in Physical scenario of the aquatic environment Table 3. The physical scenario was chosen in such a way that it was representative of the aquatic (surface water) water bodies of the Application of the PRIMET model research area. For this, 3 sites were selected along the canal system and assessed during 2 field surveys in May and October 2005. For the aquatic risk assessment, the ETR was calculated using The 2 surveys were regarded as being representative of high- and the average application rate and toxicity values available for the low-flow conditions within the irrigation canals. Field measure- 17 pesticides (Tables 2 and 4) applied to the worst-case scenario. ments were taken on the geometry of the water course at all 3 Either a ‘no risk’ or ‘possible risk’ category was indicated for sites, i.e. the bottom width of water body (b), depth of water body most pesticides studied, with a ‘definite risk’ not being indicated

(h), length of water body (L) and side slope (s1), of the canal. All for any of the pesticides within the aquatic environment. measurements were taken with a tape measure and presented in meters. The average water flow velocity (v) was determined using Pesticides indicating ‘No risk’ an OTT flow meter fitted with a No. 3 calibration flow propel- ler. Triplicate water samples (250 mℓ) were collected during each The PRIMET model calculated low ETR values (< 1) for cyro- survey from the 3 sites and the temperature, mass fraction organic mazine (ETR = 0.00014; PEC = 0.12 μg/ℓ); bentazone (ETR = matter in suspended solids (mom) and mass concentration of sus- 0.00049; PEC = 1.1 μg/ℓ); alachlor (ETR = 0.061; PEC = 3.5 μg/ℓ); pended solids (SS) in water were measured using the filtration and endosulfan (ETR = 0.067; PEC = 0.0026 μg/ℓ); captan (ETR = dying techniques described by Wepener and Vermeulen (2005). 0.2; PEC = 0.25 μg/ℓ); pendimethalin (ETR = 0.3; PEC = 0.84 The temperature (T) was recorded on site using a hand-held ther- μg/ℓ); abamectin (ETR = 0.45; PEC = 0.0068 μg/ℓ) and fenami- mometer. phos (ETR = 0.46; PEC = 3.1 μg/ℓ) indicating no predicted risks of these compounds to the aquatic ecosystem (Table 4). Application scheme Cyromazine was found to have been frequently used in rela- The required input data for PRIMET regarding application tively small quantities (136.64 g a.i./ha). It was applied at an inter- scheme (i.e. the number of applications (n), the individual appli- val of 7 d on average 6 times in a season in the study area, where cation dosage (M) and the frequency between applications (∆t)) it was mainly used on spinach. Numerous laboratory and field were obtained directly from the farmers, experts and pesticide studies conducted demonstrate that cyromazine was efficiently consultants within the area. The percentage spray drift (% drift) degraded by biological mechanisms and had a low toxicity to was predicted using the IMAG Drift Calculator v1.1 (Holterman aquatic organisms (Tomlin, 2000). Normal application of benta-

640 Available on website http://www.wrc.org.za ISSN 0378-4738 = Water SA Vol. 34 No. 5 October 2008 ISSN 1816-7950 = Water SA (on-line) TABLE 4 Pesticides used in the mixed agriculture study area and relevant application rates assessed from field surveys as well as the recommended dosage applied Active ingredi- a Applied/ b Applica- c Number of d Spray drift PEC 1 PEC n PNEC water ETR ent Recom- tion applica- (%) water water (PEC/PNEC) mended interval tions (µg/ℓ) (µg/ℓ) dose (days) (g a.i./ha) Deltamethrin 29 3 5 1 0.021 0.037 0.0005 75 Cypermethrin 112 7 6 1 0.08 0.2 0.0036 55 Parathion 813 7 2 1 0.58 0.58 0.03 20 Dichlorvos 861 21 2 1 0.63 0.63 0.033 19 Carbaryl 2 513 7 3 1 1.8 3.7 0.54 6.8 Bromoxynil 420 3 2 1 0.31 0.31 0.11 2.8 Methomyl 2 943 5 8 1 2.1 5.1 3.7 1.4 Linuron 1 495 7 2 1 1.1 1.1 0.7 1.6 Aldicarb 17 803 365 1 1 13 13 9.5 1.4 Fenamiphos 4 221 30 3 1 3.1 3.1 6.6 0.46 Abamectin 6.1 14 2 1 0.0044 0.0068 0.015 0.45 Pendimethalin 1 359 45 2 1 0.84 0.84 2.8 0.3 Captan 348 10 6 1 0.25 0.25 1.3 0.2 Endosulfan 864 10 3 1 0.0012 0.0026 0.039 0.067 Alachlor 3 402 45 2 1 2.5 3.5 57 0.061 Betazone 1 508 7 2 1 1.1 1.1 2300 0.00049 Cyromazine 137 7 6 1 0.099 0.12 900 0.00014 a Average pesticide application dose according to spraying programme. b Application interval c Number of applications d Spray drift calculated using IMAG drift calculator zone was considered unlikely to be hazardous to most non-target area. According to the US EPA (1990) abamectin is highly toxic to organisms because of its negligibly low toxicity (based on avail- aquatic invertebrates. Concentrations in the water, however, are able data) and low application rates (US EPA, 1985; Huber and expected to be low mainly due to photodegradation and adsorp- Otto, 1994). The high PNEC values for cyromazine (900 μg/ℓ) tion of this pesticide by sediments. It is also rapidly degraded by and bentazone (2300 μg/ℓ) were a consequence of their overall soil micro-organisms and becomes less toxic to aquatic organisms lower toxicity to aquatic organisms. They were therefore regarded (Wislocki et al., 1989). This pesticide does not bioaccumulate as posing a low risk to the aquatic ecosystem. (Tomlin, 2000). Fenamiphos is mainly applied to grapes, citrus, Alachlor and pendimethalin are pre-emergence herbicides tobacco, soya beans, beans and vegetables (cabbage, lettuce and and were found to have been extensively used within the study peppers). It readily degrades in water and is also degradable on area. Alachlor has a moderate persistence and is moderately toxic soil surfaces. Based on Koc values and leaching studies, fenami- to fish and aquatic invertebrates and is highly toxic to primary phos can be classified as a compound with low mobility (Tomlin, producers (Johnson and Finley, 1980; US EPA, 1997). It was 2000). Fenamiphos is regarded as having a high toxicity to aquatic found to be mainly used on maize crops within the study area. organisms but will not substantially bioaccumulate (Smith, 1993). Pendimethalin was mainly applied to tobacco in the study area. Last of the pesticides indicated to pose no risk is the fungicide, Pendimethalin does not represent a high risk to aquatic animals captan, which is mainly applied to sunflowers, grapes, and onions and plants. According to the US EPA R.E.D. fact sheet (1997) in the study area. Captan is known to be slightly toxic to aquatic pendimethalin has a high affinity to bind to soil and sediment par- invertebrates and has a moderate tendency to bioaccumulate in ticles and should limit concentrations in surface water. Endosul- living tissue of aquatic organisms (Tomlin, 2000). Captan is non- fan is an organochlorine insecticide applied to a number of crops volatile and rapidly hydrolyzes in water and in this way the risk of in the study area including tobacco, vegetables, citrus, grapes this chemical to aquatic life is reduced (Wolfe et al., 1976). and maize. Endosulfan is highly toxic to both fish and aquatic invertebrates (Tomlin, 2000). Although it readily degrades in Pesticides indicating a ‘possible risk’ water, it binds strongly to sediments and can be slowly released back into the water column. This produces low concentrations for Analysis of the application patterns of aldicarb (ETR = 1.4; PEC many months (Peterson and Batley, 1991). Bollmohr et al. (2007) = 13 μg/ℓ); methomyl (ETR = 1.4; PEC = 5.1 μg/ℓ); linuron (ETR reported an endosulfan environmental concentration of 0.16 µg/ℓ = 1.6; PEC = 1.1 μg/ℓ); bromoxynil (ETR = 2.8; PEC = 0.31 μg/ℓ); in water. This value was above the PEC value predicted in this carbaryl (ETR = 6.8; PEC = 3.7 μg/ℓ) dichlorvos (ETR =19; PEC study. This is probably attributed to differing scenarios, but is = 0.63 μg/ℓ); parathion (ETR = 20; PEC = 0.58 μg/ℓ) and two most likely due to the lower application rate found in this study pyrethroids, cypermethrin (ETR = 55; PEC = 0.2 μg/ℓ) and del- area. Abamectin was applied in relatively small dosages (6.14 g tamethrin (ETR = 75; PEC = 0.037 μg/ℓ) indicated the possibil- a.i./ha) and was mainly applied to vegetables (lettuce, spinach ity of a risk occurring at their respective predicted environmental and peppers), onions, grapes, maize, and potatoes in the study concentrations calculated by the PRIMET model (Table 4).

Available on website http://www.wrc.org.za 641 ISSN 0378-4738 = Water SA Vol. 34 No. 5 October 2008 ISSN 1816-7950 = Water SA (on-line) Aldicarb is a carbamate insecticide, nematicide and aca- than these estimates (0.2 μg/ℓ) thus resulting in a higher ETR of ricide that is applied to tobacco, grapes, beans, potatoes, citrus 55. This value translates into higher potential risk. Deltamethrin fruit, cotton and sorghum at very high single dose applications is also a pyrethroid insecticide that is highly toxic to fish under (17 803 g a.i./ha) in the study area, resulting in a high predic- laboratory conditions using a continuous exposure but is not tive environmental concentration. PRIMET takes into consid- toxic to fish under natural conditions using a natural exposure eration multiple or single applications and does not determine regime. Deltamethrin undergoes microbial degradation within 1 the cumulative pesticide inputs from various farmers spraying to 2 weeks of application (Tomlin, 2000) and was mostly used on simultaneously. Volatilisation from water and bioaccumulation in cotton, maize, spinach, grapes, and soya beans as well as for ani- animal tissue do not seem to be an important fate process due to mal husbandry. Only low dosages were, however, used (29 g a.i./ its high solubility in water. Aldicarb is a highly toxic pesticide ha) in the study area on these crops. Toxicity to aquatic arthro- (PAN, 2006) that may have a significant chronic effect on aquatic pods is indicated to present an environmental hazard if appli- biota (Foran et al., 1985). Aldicarb will pose a potential risk to cations are intensive (Solomon et al., 2001). The relatively low the aquatic system due to its persistent nature in water and soils. PNEC for these 2 pyrethroid pesticides is a consequence of their The PEC value of 13 μg/ℓ given in this study area is higher than high toxicity to aquatic arthropods and fish. Pyrethroids (such as the Canadian Water Quality Guideline values (reported as 1 μg/ℓ cypermethrin and deltamethrin) are highly hydrophobic and have for the protection of aquatic life) and will pose a potential risk to large octanol-water coefficients (Kow). They would bioconcentrate the aquatic ecosystem. Linuron is a herbicide applied mainly to within non-target organisms in the water matrix, resulting in tox- carrots, celery, soya beans and potatoes at a rate of 1 000 g a.i./ icity to both aquatic invertebrates and fish (Van der Werf, 1996; ha. Microbial degradation is the primary factor in disappearance Maund et al., 1997; Solomon et al., 2001). The PRIMET model of this substance from soil. Linuron was recorded to be highly therefore indicated that the highest possible risk to aquatic organ- toxic to aquatic plants (US EPA, 1984) and according to Cuppen isms will be eminent for these two substances with ETR values et al. (1997), under normal application in agricultural fields, will of 55 and 75 at 0.2 and 0.037 μg/ℓ environmental concentrations not greatly affect species composition of invertebrates in adja- for cypermethrin and deltamethrin, respectively. Pyrethroids are cent aquatic ecosystems. Methomyl is an insecticide and acari- considered to pose a lesser risk under field conditions as they dis- cide applied to vegetables (cabbage, lettuce and spinach), tobacco, play high adsorption properties (Schroer et al., 2004). grapes, onions, potatoes, citrus, wheat and sorghum. It is indi- The pesticides in this mixed agricultural area belong to a vari- cated to have a moderate to high toxicity for fish, being even more ety of groups (namely insecticides (11 of 17), herbicides (5 of 17) toxic to invertebrates. Bromoxynil is a herbicide used for pests and fungicides (1 of 17). According to the environmental prelimi- associated with maize, wheat, sorghum and onions. It is known to nary risk assessment, 9 of these agrochemicals are indicated to be highly toxic to zooplankton and moderately toxic to molluscs. have some degree of impact or effect on non-target aquatic organ- Carbaryl is a carbamate insecticide that is used on a variety of isms present in the water bodies bordering the fields. The largest crops to mainly control sucking insects and is also toxic to ben- risk to this system was calculated for the scenario where deltam- eficial insects (Tomlin, 2000). It is highly toxic to invertebrates; ethrin was used (Table 4). but has a lower toxicity to fish. Carbaryl is known to accumulate in aquatic organisms (EXTOXNET, 2004). This risk is lowered PERPEST results under alkaline conditions (such as those found within the study area) (Baron, 1991). It is applied to a variety of crops in the study The PERPEST model was applied to the pesticide crop combina- area including citrus crops, tobacco, grapes and some vegetables. tion for which only a potential or definite risk was indicated in Under this specific scenario and application regime, a PEC value the first-tier calculation. This analysis yielded the probability of of 3.7 μg/ℓ was predicted to occur in the study area. Dichlovos observing a clear effect for the 8 effect classes for the response is an insecticide and acaricide used in the study area on spinach, variables for 7 insecticides (Table 5) and 2 herbicides (Table 6). cabbage, other vegetable crops and on grapes. The Environmental Deltamethrin (PEC of 0.037 μg/ℓ) showed the highest ETR value Health Criteria (EHC) review concluded that, with the exception and therefore a clear effect on insects and macro-crustaceans with of gross spillage, recommended use does not constitute a hazard a 100% probability of occurrence shown. The probability of clear for aquatic and terrestrial organisms (Tomlin, 2000). It is non- effects was indicated as 55% and 23% for the micro-crustacean persistent in the environment with rapid decomposition in the and rotifer group endpoints, respectively. Probability of clear atmosphere (Tomlin, 2000). Parathion is classified as a compound effects observed for cypermethrin at the relevant PEC value for with low mobility based on Koc values and leaching studies. Par- macro-crustaceans was 100%, 96% for insects, 51% for micro- athion is rapidly degraded under laboratory and field conditions crustaceans and 27% for rotifers. Other notable pesticide effects on (Tomlin, 2000). It is an organophosphate insecticide and acaricide aquatic insects were indicated for carbaryl, parathion and dichlor- which has a moderate toxicity to aquatic invertebrates and fish vos, with 33%, 27% and 19% probability of clear effects, respec- (Mulla and Mian, 1981). Parathion was applied to vegetables (cab- tively. The effects of these insecticides on micro-crustaceans bage, cucumber and lettuce), grapes, tobacco, maize, and cotton were 29%, 45% and 29% probability of clear effects, respectively. within the study area. Cypermethrin is a pyrethroid insecticide Aldicarb and methomyl did not indicate a clear effect towards used on cabbage, lettuce and onions as well as tobacco and maize all aquatic communities due to no high clear effects shown. Bro- in the study area. The EHC concluded that biological degrada- moxynil did not indicate clear effects on macrophytes, fish and tion is rapid, with the subsequential levels of cypermethrin and tadpoles, macro-crustaceans and insects, molluscs, phytoplank- its degradation products in soil and surface waters being very low ton and zooplankton effect classes. Linuron resulted in values of (Tomlin, 2000). Thus, under field conditions, fish are not expected 20%, 20% and 22% of clear effects for community metabolism, to be at risk from normal agricultural usage of cypermethrin even phytoplankton and macrophytes affect classes, respectively. A though it is classified as being highly toxic to invertebrates and low clear effect (9%) was observed for bromoxynil in periphy- vertebrates. Bollmohr et al. (2007) indicated an acute exposure ton communities at 0.31 μg/ℓ. Community metabolism, algae, fish toxicity ratio of 0.01 based on calculated concentrations found in and other micro-invertebrates were impacted to a lesser extent by water of 0.001 μg/ℓ. The PEC value given by PRIMET was higher any of the tested insecticides.

642 Available on website http://www.wrc.org.za ISSN 0378-4738 = Water SA Vol. 34 No. 5 October 2008 ISSN 1816-7950 = Water SA (on-line) The first-tier risk-assessment approach used in this study is of safety factors. It is also especially useful in developing coun- based on a worst case scenario using data from short-term stand- tries such as South Africa where such models have not yet been ard laboratory tests. These tests aim at maximum and continu- applied. These models are able to assist farmers in reducing their ous exposure of the organisms (De Jong et al., 2005), whereas environmental risks in terms of pesticide usage in a user- friendly in higher-tier methods like the PERPEST effect model, toxicity and cost-effective manner. This is done by giving them an indica- data are based on more realistic exposure and involves a higher tion of which pesticides at what dosages and applications pose the degree of biological complexity. This may incorporate a degree lowest risk potential in their particular scenarios thereby making of resilience in the ecosystem that is not determined in single- for a more responsible environmental choice of pesticide usage. species toxicity tests (De Jong et al., 2005). It is therefore essential Both models have ample scope for modelling ecological risk to generate additional data on potential effects and exposure to assessment in South Africa. further refine the risks posed to this particular system. By following the TRIAD approach (Chapman, 2000), conducting labora- Acknowledgments tory (bioassays and meso- or microcosm experiments) and field assessments such as chemical monitoring as well as implement- South African Netherlands Research Programme on Alternatives ing biomonitoring techniques, a more refined risk assessment can in Development (SANPAD), which is a collaborative research pro- be executed. This will form an important component in validating gramme financed by the Netherlands Ministry of Foreign Affairs, the findings of this preliminary risk assessment. By implement- has generously funded this research with the aim of improving ing these techniques it can be determined how local aquatic spe- pesticide use and establishing their effects on the environment cies, populations or communities inhabiting these ecosystems are and on the communities associated with agriculture. Additional affected. financial support was obtained from the National Research Foun- Data on each of the pesticides’ intrinsic properties were taken dation (NRF) and the International Foundation of Science (IFS). from literature sources available mostly for the temperate regions of Europe and North America. This has led to uncertainties in References application to warmer regions (Kwok et al., 2007). It was, however, indicated by studies conducted by Brock et al. (2000a; 2000b) BARON RL (1991) Carbamate insecticides. In: Hayes WJ Jr. and Laws and Maltby et al. (2005), that no differences in toxicity and sen- ER Jr (eds.) Handbook of Pesticide Toxicology. Vol. 3. Classes of sitivity of tropical and temperate species for selected pesticides Pesticides. Academic Press Inc, NY. BELTMAN WHJ and ADRIAANSE PI (1999) User’s Manual TOXSWA (chlorpyrifos, fenitrothion and carbofuran) could be found. It is, 1.2. Simulation of Pesticide Fate in Small Surface Waters. DLO however, imperative that higher-tier studies be conducted on pes- Winand Staring Centre, Technical Document 54. Wageningen, the ticides showing potential risks under local conditions. This would Netherlands. not only contribute to the case-base PERPEST database, but will BOLLMOHR S, DAY JA and SCHULZ R (2007) Temporal variability in determine how these models differ if more relevant, local data particle-associated pesticide exposure in a temporarily open estuary, replaced literature-based data from temperate regions. Meinhardt Western Cape, South Africa. Chemosphere 68 479-488. (2008) for example, found that pesticide half-lives determined for BROCK TCM, LAHR J and VAN DEN BRINK PJ (2000a) Ecological South African soil differed from non-localised published data. Risk Assessment of Pesticides in Freshwater Ecosystems Part 1: Her- bicides. Alterra-Report 088. Wageningen, the Netherlands. It has become evident that the application value of the PER- BROCK TCM, VAN WIJINGAARDEN RPA and VAN GEEST G PEST model in South Africa could be enhanced through using (2000b) Ecological Risk Assessment of Pesticides in Freshwater Eco- actual measured pesticide concentrations in place of PEC val- systems Part 2: Insecticides. Alterra-Report 089. Wageningen, the ues derived from other models. For example, using the results Netherlands. obtained from the extensive work carried out on the Lourens BROCK TCM, ARTS GHP, MALTBY L and VAN DEN BRINK PJ River in the Cape Province (Bollmohr et al., 2007; Schulz, 2001), (2006) Aquatic risks of pesticides, ecological protection goals and the predicted effects emanating from the PERPEST model can be common aims in EU legislation. Integr. Environ. Assess. Manage. 2 compared to actual observed effect data. This would provide ideal e20-e46. CHAPMAN PM (2000) The sediment quality triad: then now and tomor- validation for the implementation of this model within higher-tier row. Int. J. Environ. Pollut. 13 351-356. risk assessments. CUPPEN JGM, VAN DEN BRINK PJ, VAN DER WOUSE H, ZWAAR- DEMAKER N and BROCK TCM (1997) Sensitivity of macrophyte- Conclusions dominated freshwater microcosms to chronic levels of the herbicide linuron. Ecotox. Environ. Saf. 38 25-35. The PRIMET model provides an objective method of quantify- DE JONG FMW, MENSINK BJWG, SMIT CE and MONTFORTS ing and comparing risks of pesticides applied to an agricultural MHMM (2005) Evaluation of ecotoxicological field studies for area. The model was effectively used in this study to predict the authorization of plant protection products in Europe. Hum. Ecol. Risk. Assess. 11 1157-1176. risks in the aquatic scenario and was found to be user friendly due EU (1997) Council Directive 97/57/EC of September 21, 1997; Establish- to easily accessible and available data. 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